Finite impulse response (FIR) channel filters are the most commonly used type of digital equalizer for commercially produced digital receivers. FIG. 1 shows a block diagram of a system 10 employing such a filter. A digital data signal X is received in FIR filter or equalizer 12, which filters the signal X according to a filtering algorithm (discussed in more detail below) to produce an equalized data signal Y. The equalized data signal Y is processed sequentially by a sequence detector 14 and a target filter 16 to produce an ideal, noiseless value YH for the equalized data signal. The difference between equalized data signal Y and its ideal, noiseless value YH is the error by which the filtering algorithm is adapted (see the discussion below). Such filters have a transfer function that generally satisfies the following equations:FIR filter=(C0,C1, . . . Cn)  (1)Y[k]=C0*X[k]+C1*X[k−1]+C2*X[k−2]+ . . . +Cn*X[k−n]  (2)where C is a weighting coefficient and Y[k] is the output of the filter.
Adaptive FIR filter algorithms generally allow for self-optimization of channel parameters. Regular least mean squares (LMS) gradient algorithms, which are the most common technique for adaptive FIR filtering, generally include a set of update rules for the coefficients of equations (1) and (2) above as follows:C0=C0+μ*E[k]*X[k]  (3)C1=C1+μ*E[k]*X[k−n]  (4). . .Cn=Cn+μ*E[k]*X[k−n]  (5)where μ is a weighting coefficient or factor that affects or controls the adaptation rate of the filter, and E[k] is the error in the Y[k] value relative to its ideal noiseless value (e.g., YH in the flow shown in FIG. 1).
The ideal noiseless value YH is usually estimated and/or generated by convolving the output of the sequence detector with the target channel response (see step 28 in the flow chart 20 shown in FIG. 2). Thus, the next value of the coefficients Cx in equations (1) and (2) above depends on their previous value. The occasional errors in the estimated noiseless values due to detection errors do not materially impact the convergence of the regular LMS algorithm.
FIG. 2 shows a flow chart for a conventional adaptive filtering process 20 employing the system 10 of FIG. 1. The process begins with step 22, receiving a data signal X into a conventional data receiver circuit, system and/or apparatus. Step 24 generally involves passing the data X through an FIR filter (e.g., FIR filter 12 of FIG. 1) to generate equalized data signal Y. The primary function of FIR filter 12 is to output equalized data Y to a downstream receiving and/or processing device.
However, one may also train, update, correct and/or modify the filtering algorithm to ensure a desired or predetermined level of accuracy in the equalizing process. The first step of the conventional training sub-process is step 26, processing the equalized data signal in a sequence detector. Thereafter, in step 28, the sequence-detected equalized data signal is convolved with a target filter to estimate or generate YH. The target filter generally results from user selection of the channel target, which is generally an ideal sequence detector filter, in step 30. The difference between equalized data signal Y and ideal, noiseless equalized data signal YH is then calculated in step 34, and in step 36, that error information (i.e., a non-zero difference between Y and YH) is fed into the FIR filter to update and/or modify the weighting coefficients in the FIR algorithm.
The regular LMS algorithm reduces the root-mean-square (RMS) error of the E[k] values and generally results in a fairly low error rate after a period of time in which the coefficients converge. However, the lowest RMS error for Y[k] still does not equate to the lowest error rate for Viterbi detection. For example, in data receivers used in magnetic recording applications (e.g., read channel devices), errors are often dominated by simple error events such as single bit, dibit or tribit errors. Minimizing the RMS error in Y[k] indirectly minimizes these error events in some fashion, but not necessarily optimally. Conventional receivers and filtering algorithms tend to sacrifice performance in order to optimize detection of all types of error patterns. A need is thus felt for an approach that directly targets mninimizing one or more dominant or relatively frequent error events to improve or maintain detection performance and/or reduce convergence time.